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Model: Glubbot-Team/glubbot1.1-7.5m
Source: Original Platform

README.md

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+---
+license: apache-2.0
+tags:
+- text-generation
+- experimental
+- ultra-lightweight
+- tiny-stories
+model_type: llm
+language:
+- en
+datasets:
+- roneneldan/TinyStories
+metrics:
+- perplexity
+---
+
+# glubbot1.1-7.5m
+
+## Model Description
+glubbot1.1-7.5m is an experimental, ultra-lightweight text generation model developed by the **Glubbot-Team**. 
+
+With only 7.5 million parameters, this model is designed for experimental testing, low-resource inference benchmarking, and studying language acquisition in small-scale models. It was trained specifically to generate short, grammatically correct narratives.
+
+- **Developed by:** Glubbot-Team
+- **Model Type:** Transformer-based Causal Language Model
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Status:** Experimental / Testing
+
+## Intended Uses & Limitations
+This model is intended solely for research, architectural testing, and experimental evaluation. 
+
+### Intended Use
+- Synthetic text generation for short, child-like stories.
+- Benchmarking inference speeds on edge devices or CPUs.
+- Testing tokenization strategies and pipeline integration.
+
+### Limitations & Biases
+- **Domain Specificity:** Optimized purely for short stories; it cannot handle reasoning, coding, or factual factual queries.
+- **Limited Capacity:** Due to the 7.5M parameter size, the model may produce repetitive structures or drift logically during long generations.
+- **Not for Production:** Do not deploy this model for general-purpose assistant tasks.
+
+## Training Details
+
+### Training Data
+The model was trained on the **TinyStories** dataset (`roneneldan/TinyStories`). This dataset consists of short stories containing words typically understood by 3- to 4-year-olds, optimized to teach models basic grammar and reasoning.
+
+### Hyperparameters
+- **Context Length:** [e.g., 512 / 1024]
+- **Batch Size:** [Insert Batch Size]
+- **Learning Rate:** [Insert Learning Rate]
+- **Optimizer:** AdamW
+
+## Evaluation
+Evaluation metrics are monitored primarily to test convergence stability on the TinyStories split. The model is evaluated on its ability to maintain basic grammatical coherence.